Recognising Biomedical Names: Challenges and Solutions

TL;DR

This paper addresses challenges in biomedical named entity recognition by proposing a transition-based model, data augmentation techniques, and a method for selecting suitable pre-training data to improve recognition of complex and discontinuous biomedical names.

Contribution

It introduces a transition-based NER model for discontinuous mentions, a cost-effective approach for selecting pre-training data, and data augmentation methods tailored for biomedical NER.

Findings

Transition-based model improves recognition of discontinuous mentions.

Data augmentation enhances NER performance with limited labeled data.

Selecting in-domain pre-training data boosts model accuracy.

Abstract

The growth rate in the amount of biomedical documents is staggering. Unlocking information trapped in these documents can enable researchers and practitioners to operate confidently in the information world. Biomedical NER, the task of recognising biomedical names, is usually employed as the first step of the NLP pipeline. Standard NER models, based on sequence tagging technique, are good at recognising short entity mentions in the generic domain. However, there are several open challenges of applying these models to recognise biomedical names: 1) Biomedical names may contain complex inner structure (discontinuity and overlapping) which cannot be recognised using standard sequence tagging technique; 2) The training of NER models usually requires large amount of labelled data, which are difficult to obtain in the biomedical domain; and, 3) Commonly used language representation models are pre-trained on generic data; a domain shift therefore exists between these models and target biomedical data. To deal with these challenges, we explore several research directions and make the following contributions: 1) we propose a transition-based NER model which can recognise discontinuous mentions; 2) We develop a cost-effective approach that nominates the suitable pre-training data; and, 3) We design several data augmentation methods for NER. Our contributions have obvious practical implications, especially when new biomedical applications are needed. Our proposed data augmentation methods can help the NER model achieve decent performance, requiring only a small amount of labelled data. Our investigation regarding selecting pre-training data can improve the model by incorporating language representation models, which are pre-trained using in-domain data. Finally, our proposed transition-based NER model can further improve the performance by recognising discontinuous mentions.